Knowledge-Based Agent for Efficient Allocation of Distributed Resources

Part of the Studies in Computational Intelligence book series (SCI, volume 511)


The mobilization of heterogeneous distributed resources and the allocation of resources to user-tasks are still challenges in distributed computing. In this paper, we propose a knowledge-based agent to solve the problem of resources mobilization and allocation taking into account the continuous changing of resources conditions. The agent model we propose is mainly based on ontological models representing basic collaborative environment entities, rules, inference engines, and object oriented components for resources data processing.We suggest mechanisms to handle the discovered resources in terms of updating, filtering, ranking, and allocation.


Ontology Resources management Task-based computing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Murugan, B.S., Lopez, D.: A Survey of Resource Discovery Approaches in Distributed Computing Environment. International Journal of Computer Applications 22, 44–46 (2011)CrossRefGoogle Scholar
  2. 2.
    Sharma, A., Bawa, S.: Comparative Analysis of Resource Discovery Approaches in Grid Computing. Journal of Computers 3, 60–64 (2008)CrossRefGoogle Scholar
  3. 3.
    Ngan, L.D., Kanagasabai, R.: Semantic Web service discovery: state-of-the-art and research challenges. Personal and Ubiquitous Computing, 1–12 (2012)Google Scholar
  4. 4.
    Di Modica, G., Tomarchio, O., Vita, L.: Resource and service discovery in SOAs: A P2P oriented semantic approach. Int. J. Appl. Math. Comput. Sci. 21, 285–294 (2011)CrossRefMATHGoogle Scholar
  5. 5.
    Eftychiou, A., Vrusias, B.: A Knowledge-Driven Architecture for Efficient Resource Discovery in P2P Networks. In: 2010 2nd International Conference on Intelligent Networking and Collaborative Systems (INCOS), pp. 467–472 (2010)Google Scholar
  6. 6.
    Liu, J.: World Wide Wisdom Web (W4) and Autonomy Oriented Computing (AOC): What, When, and How? In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 157–159. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Haeupler, B., Pandurangan, G., Peleg, D., Rajaraman, R., Sun, Z.: Discovery through gossip. In: Proceedings of the 24th ACM Symposium on Parallelism in Algorithms and Architectures, pp. 140–149. ACM Press (2012)Google Scholar
  8. 8.
    Anya, O., Tawfik, H., Amin, S., Nagar, A., Shaalan, K.: Context-aware knowledge modelling for decision support in e-health. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2010)Google Scholar
  9. 9.
    Sasa, A., Juric, M.B., Krisper, M.: Service-Oriented Framework for Human Task Support and Automation. IEEE Transactions on Industrial Informatics 4, 292–302 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ebrahim Nageba
    • 1
  • Mahmoud Barhamgi
    • 1
  • Jocelyne Fayn
    • 2
  1. 1.Université Lyon 1VilleurbanneFrance
  2. 2.SFR Santé Lyon EstUniversité Lyon 1BronFrance

Personalised recommendations